Relevancy of communication is paramount in developing relationships with customers. Assume two customers buy the same product. Do you communicate to them in the same way? The answer lies in properly identifying, mining and applying third-party data. It will facilitate more relevant, meaningful communication streams and improve consumer relationships.
Relying solely on house file data comprised of self-reported demographic and observed purchase behavior limits marketers’ efforts to create meaningful dialogues. For example, two consumers, Taylor and Adam, purchase a digital camera. Taylor, a 55-year-old Hispanic woman with four kids, is an avid scrapbooker. Her offer should feature pictures of Hispanic families, focusing on products to help her capture the special moments in her life.
Adam is single and just bought a new house, drives a luxury car, has an annual income of $120,000 and spent $5,000 each year on retail for the last five years. His offer should focus on high-end home theater equipment and appliances. The purchase of the same item resulted in two separate and more meaningful communications.
It is important to first define your marketing goals and then identify the data you need. There are basically four types of data available for appends: demographic, psychographic, life-stage and transactional.
Demographic data will provide a customer’s age, income, family size, education and employment status. Psychographic data will enable better understanding of consumer interests and general mindset. Life-stage data identify key life-changing triggers that can personalize a communication by sending the right message to the right person at the right time. With this type of data, it is important to understand if the data identifies the life-stage at the pre, during or post stage. If you want to target mothers before their babies are born, use prenatal data. If you want to target people 60 days after they move, review the timing of the new-mover data you rent.
The most powerful predictor of retail purchases is third-party transactional data: it will reveal where people are actually spending their money. It is one thing for a consumer to say that they enjoy photography in an online survey and another thing to know that they spent $7,000 on photography equipment from three different retailers in the last six months. Transactional data that are gathered across multiple product categories from multiple retail sources give marketers a multi-dimensional view of customers.
Frequency of industry purchases makes possible line extension opportunities, and insights into consumer affinities for marketing communications are possible when third-party transactional data is properly leveraged. Research has shown that, on average, nearly 90 percent of the most meaningful variables used to build high-performing direct response models are transactional in nature, with demographic and psychographic variables comprising the remainder.
In addition to knowing which data will best meet your needs, it is important to identify the right data vendor. Append bad data and you could do more harm than good.
Source. Identify vendors who are original compilers of data versus those that broker or that allow the hygiene and collection of the data to be handled by outside parties. The further the data is from the original source, the greater chance of error.
Definition. Make sure you understand the true definitions of data you are getting. Some may offer “Hot Line” information, and although it is reported to be 30 days fresh, it may have taken 60 days for it to be gathered and processed, meaning that the data is really three months old. Definitions of variables can vary from source to source.
Coverage. When data providers don’t have actual data, they will infer or create model scores to determine the likelihood of the information. Know the percentage of actual versus inferred data.
Third-party data have a proven history, and with costs ranging from 4 cents to 6 cents per record matched, the marketing investment is more than justified. When properly sourced, matched and used, analytic models perform better, communications are more meaningful and relationships can turn into loyalty and evangelism.
